Collaborative Computing: Networking, Applications and Worksharing. 14th EAI International Conference, CollaborateCom 2018, Shanghai, China, December 1-3, 2018, Proceedings

Research Article

A Location Spoofing Detection Method for Social Networks (Short Paper)

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  • @INPROCEEDINGS{10.1007/978-3-030-12981-1_9,
        author={Chaoping Ding and Ting Wu and Tong Qiao and Ning Zheng and Ming Xu and Yiming Wu and Wenjing Xia},
        title={A Location Spoofing Detection Method for Social Networks (Short Paper)},
        proceedings={Collaborative Computing: Networking, Applications and Worksharing. 14th EAI International Conference, CollaborateCom 2018, Shanghai, China, December 1-3, 2018, Proceedings},
        proceedings_a={COLLABORATECOM},
        year={2019},
        month={2},
        keywords={Location spoofing Social networks Semantic analysis},
        doi={10.1007/978-3-030-12981-1_9}
    }
    
  • Chaoping Ding
    Ting Wu
    Tong Qiao
    Ning Zheng
    Ming Xu
    Yiming Wu
    Wenjing Xia
    Year: 2019
    A Location Spoofing Detection Method for Social Networks (Short Paper)
    COLLABORATECOM
    Springer
    DOI: 10.1007/978-3-030-12981-1_9
Chaoping Ding1,*, Ting Wu1,*, Tong Qiao1,*, Ning Zheng,*, Ming Xu,*, Yiming Wu1,*, Wenjing Xia1,*
  • 1: Hangzhou Dianzi University
*Contact email: 161050056@hdu.edu.cn, wuting@hdu.edu.cn, tong.qiao@hdu.edu.cn, nzheng@hdu.edu.cn, mxu@hdu.edu.cn, ymwu@hdu.edu.cn, 161050051@hdu.edu.cn

Abstract

It is well known that check-in data from location-based social networks (LBSN) can be used to predict human movement. However, there are large discrepancies between check-in data and actual user mobility, because users can easily spoof their location in LBSN. The act of location spoofing refers to intentionally making false location, leading to a negative impact both on the credibility of location-based social networks and the reliability of spatial-temporal data. In this paper, a location spoofing detection method in social networks is proposed. First, Latent Dirichlet Allocation (LDA) model is used to learn the topics of users by mining user-generated microblog information, based on this a similarity matrix associated with the venue is calculated. And the venue visiting probability is computed based on user historical check-in data by using Bayes model. Then, the similarity value and visiting probability is combined to quantize the probability of location spoofing. Experiments on a large scale and real-world LBSN dataset collected from Weibo show that the proposed approach can effectively detect certain types of location spoofing.